Interpretable prognostic modeling for long-term survival of Type A aortic dissection patients using support vector machine algorithm.

ABSTRACT:

Objectives

This study aims to develop a reliable and interpretable predictive model for long-term survival in Type A aortic dissection (TAAD) patients, utilizing machine learning (ML) algorithms.

Methods

We retrospectively reviewed the clinical data of patients diagnosed with TAAD who underwent open surgical repair at the First Affiliated Hospital of Chongqing Medical University, from September 2017 to December 2020, and at the Chongqing University Central Hospital between October 2019 and April 2020. Cases with less than 20% missing data were imputed using random forest algorithms. To identify significant prognostic factors, we performed LASSO (Least Absolute Shrinkage and Selection Operator) Cox regression analysis, including preoperative blood markers, previous medical hi

SUBMITTER: Cai H 

PROVIDER: S-EPMC11998247 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature